Search

The flow forecast is used in several sectors of society, bringing benefits in relation to the mitigation of possible impacts in flood events and it is information of great value for the economic sectors associated with agriculture and energy generation. In South America, climate and meteorological variability directly impact these economic sectors. In Brazil, for example, the production of electricity is predominantly hydroelectric generation, which currently represents about 63% of the installed power in the country, in addition to the complementarity between different hydrographic basins and the other sources that make up the Brazilian energy matrix.The Brazilian electricity sector relies on flow forecasts for different time scales, which are used to optimize the available water resources and for the energy commercialization. The National Electric System Operator (ONS) is responsible for coordinating the operation of 153 Hydroelectric Power Plants (HPPs) and uses different hydrological models for flow forecasting. For the 14-day horizon (short term) it’s used the deterministic rain-flow model called SMAP. For the horizon of 15 to 45 days (sub seasonal) it’s used the PREVIVAZ, a univariate stochastic model.This work presents the evaluation of the performance of the SMAP model for forecasting in a sub seasonal horizon for 6 reservoirs in the Iguaçu River basin, associated with HPPs with a total installed capacity of 7,024 MW, located in the southern region of Brazil. Streamflow forecasts were evaluated using the European Center for Medium-Range Weather Forecasts (ECMWF) sub seasonal forecast, with lead time up to 46 days, from the Subseasonal-to-Seasonal (S2S) project database, and using the Global Ensemble Forecast System (GEFS) sub seasonal forecast, with lead time up to 35 days, from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA).The results showed that the flow forecasts for the sub seasonal horizon present good performance for the initial forecast horizon, with degradation in the quality of the results after this horizon. There was also evidence of gain associated with forecasts for the ensemble over the entire horizon. The use of the SMAP model combined with precipitation forecasts in the sub seasonal horizon proved to be superior to the PREVIVAZ model, currently in use at the National Electric System Operator (ONS), with a significant improvement being observed, evidencing the usefulness of flow forecasts based on numerical models of precipitation prediction for the sub seasonal horizon.Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian National Electricity Regulatory Agency (ANEEL) under its Research and Development Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).

Long-term planning is part of good water resource management. This management takes place according to the needs of society, such as optimizing energy production, providing water for agriculture, supplying industry and guaranteeing urban water supply. The quantification of water resources and their long-term forecast are tools that help with this planning. In this work, we evaluated the ability of seasonal streamflow forecasts to detect droughts periods based on forecast evaluation rules of a Hydrologic Ensemble Prediction System (H-EPS), and compare them with the benchmark Ensemble Streamflow Prediction (ESP). Seasonal forecasts of seven months horizon were generated using the MGB-AS model forced with ECMWF seasonal precipitation forecasts (SEAS5), for basins larger than 1000 square kilometers. The focus of the study were the 153 hydroelectric plants of the Brazilian National Interconnected System (SIN), which affluent flows were studied for the period of 2007 to 2016. For each plant, drought forecasts were analyzed using the Area Under the Receiver Operating Characteristics curve (AUROC). The drought threshold was defined as the 90th percentile of the flow, using data from 1979 to 2006. Exceedance diagrams were made, where each forecast horizon was represented with the percentage of members that indicated the occurrence of a dry month. Then, contingency tables were set up, considering the drought detection criterion as at least one member indicating drought in the seven horizons and six months with at least 20% of the members indicating drought. The rule was elaborated based on data from the Itaipu power plant (Paraná River) and applied to all plants. Through the results, it was observed that the H-EPS forecasts were more accurate in detecting droughts than the benchmark. In the regional analysis of the results, the rule chosen for Itaipu was also suited for the plants in the southeast region. This may have occurred because these hydroelectric plants rivers present similar hydrological behavior, related to the type of soil, evapotranspiration rates, precipitation, climate and similar relief. Our next steps involve the creation, testing and analysis of more locally and temporally optimized rules, including some that consider only the first months predicted in the analysis.

Hydrological forecasts ranging from two weeks to months in advance are critical for decision making in water resources management and economic sectors. In the subseasonal timescale, there is an opportunity to anticipate events of hydrological interest, such as periods of floods and droughts. The development of subseasonal forecasts with good quality for decision support systems is still a great challenge for the technical and scientific community, as it fits into a predictability gap between medium-range weather (3 to 15 days) and seasonal climate prediction (2 to 7 months). In South America, the climate and weather variability can represent risk to activities such as agriculture and hydropower energy production. For instance, Brazil, the larger country in terms of area and economy in the continent, has an electrical power generation matrix with 63% of hydropower and rely on weather forecasts spanning multiple timescales for its integrated system operation. This work evaluated the potential skill of subseasonal streamflow forecasts over South America based on ECMWF ensemble forecasts with lead times up to 46 days obtained from the Subseasonal-to-Seasonal (S2S) project database. A continental-scale hydrologic-hydrodynamic model was used to carry out the simulation runs for obtaining subseasonal ensemble streamflow forecasts. Forecast bias was evaluated against a reference model run (i.e., pseudo-observations) for both raw and bias corrected precipitation, and the forecast skill was evaluated against the Ensemble Streamflow Prediction (ESP) method. Forecasts and pseudo-observations were aggregated into weekly averages, ranging 6 weeks for verification, and were divided into subsets for each season of the year (DJF, MAM, JJA, SON) to access seasonal patterns over South American regions. The results highlight that the forecast skill is dependent on initialization month, season, basin, and forecast lead time, with greater skill on shorter lead times. Bias correction was able to reduce the mean forecast error over most regions of the continent. In addition, the bias correction improved skill and maintained positive skill after the third week of forecast, especially in northeastern regions and on wet seasons (DJF, MAM), meanwhile in central regions the improvements were not clear. However, the ESP method outperformed the ECMWF-based ensemble in many regions. Finally, the results presented here provide insights for investigations and applications of S2S forecasts in the operational scope on a continental scale, which can bring benefits, for example, in the optimization of the operation of electricity generation reservoirs.Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian Agency of Electrical Energy (ANEEL) under its Research and Development program Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491-0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the Brazilian Electricity Regulatory Agency (ANEEL) under its research and development programme, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days, at hydro power enterprises, which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). We present in this work the framework built up as interface to the results of this project, which integrate shapefiles of main river basins in Brazil, hydro meteorological information, forecasts of precipitation from seasonal models (e.g., ECMWF’s SEAS5) and derived streamflow from hydrological model used in the project (MGB-SA mainly) for the entire electric energy network of the country. The platform encompasses layers of maps and graphics synchronized by date, respectively to locations of hydro power plants in Brazil, which allows users to perform multiple analysis for either energy planning or routine hydraulic operations. We shall demonstrate examples of applications, such analysis of a flood event happened during the 2014/2015 El Niño episode, which caused heavy precipitation, increased river level and flow into reservoirs in the Iguaçu River basin, disruption of services and economic losses in the South of Brazil. Given the limitations of seasonal precipitation forecasting, the model was successful in predicting the heavy accumulated rainfall in the analyzed period. In parallel, the hydrological model was able to simulate flow peaks well in advance. In addition, the platform allows an overview of the SIN subsystems and respective stored energy, which allows intercomparison and pragmatic analysis of the country's electric energy capacity.

Short-to-medium range streamflow forecasting is essential for planning and operating hydropower plants (HPPs). The Brazilian National Interconnected System (SIN) is composed of more than 150 HPPs that are located over a wide range of climate and hydrological conditions. Forecasts of natural inflow into the SIN reservoirs are important to establish optimal operating rules to reduce costs with other energy sources, therefore influencing the prices in the energy market. The objective of this work is twofold: (i) evaluate the skill of ensemble streamflow forecasts for the SIN hydropower plants based on continental-scale hydrological modeling (MGB-SA) and medium-range ECWMF rainfall forecasts (MGB-ECMWF), and (ii) compare the MGB-ECMWF forecasts to those produced operationally by the Electric System National Operator (ONS). The MGB-ECMWF predictions were additionally bias-corrected and updated using quantile mapping and auto-regressive model approaches, and were assessed in the period from 2015 to 2020 in terms of weekly averages. The forecast skill was estimated relative to both streamflow climatology and persistency using the CRPS metric, while the comparison between MGB-ECMWF and operational forecasts was performed using deterministic metrics typically adopted by ONS. The skill of MGB-ECMWF forecasts was substantially improved (especially in the first week) by the use of output correction methods, which were demonstrated to be essential for quantitative streamflow forecasting using a continental-scale hydrological model. The relative performance between ONS and MGB-ECMWF forecasts was quite variable (exhibiting positive and negative values) over the geographical extent of the SIN, although in several locations the MGB-ECMWF forecasts have performed equal to or even better than those issued by ONS. Finally, the results presented here provide insights for investigations and applications of streamflow forecasts using continental-scale modeling and simple output correction techniques, which can bring benefits, for example, in the optimization of the reservoir operation and electricity generation.Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian Agency of Electrical Energy (ANEEL) under its Research and Development program Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491-0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the National Agency for Electric Energy (ANEEL) under its research and development programme, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). The SIN is composed of more than 150 hydropower plants and reservoirs located over a wide range of climate and hydrological conditions. It is responsible for more than 50% of the total electricity produced in the country. In this work we describe the overall characteristics of this project, comprising its structure, main research results and its usefulness for assisting decision makers in the field of energy, as will be demonstrated through some application sceneries. We used the precipitation short-medium-range, sub-seasonal, and seasonal ECMWF forecasts as input to a continental-scale, hydrologic hydrodynamic model (MGB-SA) to produce streamflow forecasts for the SIN hydropower reservoirs. On the short-medium-range horizon we used persistency and the control member as benchmarks, while in the sub-seasonal and seasonal we used the ESP. On the short-medium-range and sub-seasonal, the ensemble average performance was superior to the control deterministic predictions for ECMWF (MGB-SA), both for the prediction quality metrics and for the event discrimination metrics. On the seasonal forecast, the ECMWF results were consistently superior to the benchmark on the first lead time month, decreasing performance with the horizon. The results of the project are expected to benefit energy generation planning, routine and emergency hydraulic operation (e.g., flood and droughts), as well as energy commercialization procedures in the country.

Hydrological forecasting is of global importance, especially with the spotted increasing trend of flood-related disasters as seen in the last two decades. The causative rainfall events of these extreme events are primarily analysed in a one-dimensional method. However, through an object-based approach, more data on these rainfall fields can be generated and studied to link them to the hydrological response observed. Through an object-based methodology ST-CORA, features from rate of change of rain intensity in space and time can be extracted by simple visual inspection. Every side of an object provides time variations that can be used as images that contain features not easy to extract. In general, rainfall events in previous studies have used aggregated information, like the duration, area, volume, maximum intensity, and the centroid. In this work, more information is captured that describes the spatial and temporal properties of the event. The main objective of this research is to use these 3D objects and their features with a deep learning model to produce a 15-day hydrological probabilistic forecast for flood prediction.A calibrated version of a large-scale hydrological model (MGB) is used to study an Amazon subbasin. The model is forced with the 50-member perturbed forecast from the TIGGE dataset for the period 2006 to 2014 (from ECMWF). The purpose of using the large-scale model is to better capture the spatio-temporal characteristics over a wider area in an effort to reduce the uncertainty in the analysis. For data-driven models, there is a need for sufficiently large databases, in this case for both the causative rainfall events and the observed hydrological responses. As such, the first two steps relate to the data generation. The first database is developed from the daily streamflow which is generated from the calibrated hydrological model at specific locations of interest with the known higher performance metrics. Second, the ST-CORA methodology is applied to extract the features from the rainfall events in order to develop a database of the rainfall objects. Third, an analysis on the statistics of the features of the objects to understand the rainfall which occurs within the study area. The final part of the research involves the effective use of these features and objects with a deep learning model. From the average annual rainfall from 2001 to 2020, three distinct precipitation patterns are observed. For the streamflow, the subbasin shows a relatively fast response which is captured within a 15-day window.A convolutional LSTM deep learning model is developed to handle 3D rainfall objects as sequences of images representing space time sequences. The outcome of this research contributes to the end-to-end deep learning model which receives the forecasted rainfall as objects and generates a corresponding hydrograph at the area of interest for which it has been trained. A potential contribution of this Conv-LSTM network is that it may provide an efficient and automated approach for streamflow forecasting in basins where there is known complexity and non-linearity, which is especially useful for early warning systems.

Society’s increasing demand for water and the need for its long-term management have motivated efforts toward improving seasonal streamflow forecasts. Currently, seasonal climate forecasts are routinely issued in meteorological centers around the world, generating information for decision-making and seasonal streamflow forecasting (SSF) studies that are becoming more frequent. Seasonal streamflow forecast skill derives from land surface initial conditions and atmospheric boundary conditions that mostly depend on large-scale climate phenomena (such as ENSO). Thus, seasonal rainfall predictions produced by dynamic climate models that represent ocean-atmosphere interactions may have a positive impact on streamflow forecasts. In South America, seasonal streamflow forecasts are essential for the hydropower sector, which is responsible for ~65% of the electric energy produced in countries such as Brazil. In this work, we assessed seasonal streamflow forecasts over South America based on a continental-scale application of a hydrologic-hydrodynamic model and precipitation forecasts from the ECMWF's fifth generation seasonal forecast system (SEAS5). Seasonal streamflow forecasts (SEAS5-SF) were evaluated against a reference model run and forecast skill was estimated relative to the Ensemble Streamflow Prediction (ESP) method. The bias correction of SEAS5 predicted precipitation improved the performance of the seasonal streamflow forecasts, frequently turning negative skill results into near null to positive skill. Results indicate that the ESP remains a hard-to-beat method for seasonal streamflow forecasting in South America. SEAS5-SF skill was found to be dependent on initialization month, season, basin and forecast lead time, with greater skill on the initialization month lead time. Rivers where the forecast skill is higher were Amazon, Araguaia, Tocantins and Paraná. Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian Agency of Electrical Energy (ANEEL) under its Research and Development program Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).

In the model-based streamflow forecasting context, initial conditions (ICs) and meteorological forcings (boundary conditions) are two important drivers of predictability. While the meteorological forcings increasingly influence forecasts at long time horizons, ICs’ influence tends to decrease, being nonetheless the predominant source of predictability at short time horizons. Quantifying the period of time when the ICs contribute to streamflow predictability can help researchers and water managers to choose the best forecasting method for each study area and horizon, balancing computational effort and streamflow forecast accuracy. In this work we quantify IC's prevalence time over boundary conditions in natural river flow forecasts in large basins (>1000km²) in South America (SA) from the MGB-SA model, a continental and hydrodynamic version of the MGB conceptual semi-distributed hydrological model. The methodology consisted of forcing MGB-SA with null precipitation as meteorological forcing, so that all the predictability obtained from the experiments was due to ICs. Streamflow experiments had a 215-day horizon, monthly initialization, daily timestep and comprehended the period of 1990 to 2010. The results were compared to the MGB-SA streamflow simulations with MSWEP as observed rainfall. Errors from the hydrological model were thus not considered in this analysis. The prevalence (T50) was estimated by the horizon (from 1 to 215 days) when streamflow predictability was degraded by 50%, i.e. when meteorological forcings start prevailing over ICs. Predictability was estimated by the performance indicator KGE, and the T50 for each of the river reaches of MGB-SA was presented in a map. The T50 map shows that the shortest IC’s prevalence on streamflow predictability is observed on riverheads, ranging from 1 to 3 days. IC's influence increases near the main reaches of the great rivers of SA, reaching up to 10 days on the Iguaçu River and up to 20 days on the Oricono River and Araguaia River. T50 is up to 40 and 90 days on reaches of the Amazon River, Atlantic coast of North Argentina and Pantanal plains. In general, IC’s influence is higher in the main river reaches of basins with flat relief, due to their greater drainage area and the slow response time of the basin.

With the increase in the global population observed since the 20th century, urban centers are becoming more prominent, and its dynamic is now far from the natural. The impact of urbanization on the rainfall has been noticed since 1921 when Horton observed that cities with more than 100,000 inhabitants created favorable conditions for convective precipitation. Later, Huff and Changnon (1972) estimated an increase of 6 to 15% on average rainfall during the summer in these regions. Several other studies confirmed the trend and pointed out that on a small and medium scale, precipitation change is usually justified by the effect of heat islands. To understand these changes, high-resolution precipitation data is needed; however, due to the lack of monitored data, especially on the largest cities in the developing countries, new sources of information should be used. MSWEP is a three hourly gridded precipitation dataset, with 0.1º spatial resolution that combines data from gauges, satellite, and reanalysis-based data to provide precipitation estimates over the entire globe (Beck, 2019). In this study, MSWEP precipitation was used in order to observe the variability of intense precipitation over the Metropolitan Area of Porto Alegre in Southern Brazil, where some previous studies indicated urban effects on precipitation. Statistical analysis was performed to evaluate changes in the intense precipitation throughout the decades. The results show that the spatial distribution patterns of intense precipitation are maintained; however, in all statistics, it was possible to observe an increase in intense precipitation over the decades, that follows the increase of the urbanized area over time.